Positron emission tomography (PET) has been widely used in medicine for diagnosis and other purposes. An object, such as a patient, may be scanned with a PET system to obtain PET datasets. For reconstruction of PET images from PET datasets, various reconstruction methods have been developed. These methods may roughly be characterized into two classes: analytical methods and iterative methods.
During the reconstruction of PET images, both the forward and back projection process may take a large amount of calculation. The reconstruction by way of an iterative method may be more time-consuming because it involves multiple forward and back projection operations. In recent years, research has been conducted on parallelization of forward projection operations and back projection operations, as parallel calculations of an iterative approximation using a GPU.
Such methods may involve the usage of a shared memory or a texture memory. The shared memory or texture memory may store an image matrix, and the image matrix may be accessed by the GPU with a relatively short latency. The memory space of the shared memory or the texture memory may be relatively small, generally ranging from several kilobytes to several megabytes. However, practically, the image matrix may need more memory space due to the higher spatial resolution of the PET system and/or an increased dimension of a PET scanner. Thus, the memory space of a shared memory or a texture memory may be insufficient for processing the image matrix acquired in such a PET scanner. There is thus a need for addressing these and/or other issues.